Background Nursing staff are often exposed to a variety of occupational risk factors in the working environment, such as long working hours and heavy workload, which associated with adverse mental health outcomes. And these factors may not be randomly distributed across different levels. Objective To explore mental health risk factors of nursing staff by multilevel analysis. Methods A cross-sectional survey of nursing staff in Shanghai Municipality, Zhejiang Province, Guangxi Zhuang Autonomous Region, and Xinjiang Uygur Autonomous Region was conducted through convenience sampling from 2018 to 2021. Data were collected by self-report questionnaires. The mental component summaries of 12-Iitem Short Form Health Survey were used to evaluate the mental health status of nursing staff, and related factors were collected atindividual level, including gender, body mass index (BMI), smoking status, drinking status, working years, pain intensity of musculoskeletal disorders, and working hours per week, and at regional level, including gross domestic product (GDP) level of each province. A two-level model was established by incorporating both individual and regional factors, and deviance was used to test the goodness of fit of the model. A traditional generalized linear model was also established, and then compared with the multilevel model. Results A total of 567 nurses participated in this study, and the valid rate of questionnaire was 80.08%. The results of the multilevel model showed that the regional factor contributed 12.1% to the mental component summaries. As to the regional factor, GDP was negatively correlated with mental health of nursing staff, the adjusted OR (AOR) was −0.53 (95%CI: −0.66-−0.28). Among the factors at individual level, the mental component summaries of females were lower than those of males (AOR=−3.25, 95%CI: −4.73-−0.35); the longer the working years, the higher the mental health score (AOR=0.11, 95%CI: 0.06-0.20); working hours per week (AOR=−0.10, 95%CI: −0.14-−0.03) and pain intensity of musculoskeletal disorders (AOR=−0.05, 95%CI: −0.06-−0.03) were negatively correlated with mental component summaries. The results of the generalized linear model included the same factors as the multilevel model, but the 95%CIs of AOR of the factors in the multilevel model were narrower, and the deviation value of the multilevel model was the smallest, indicating that the goodness of fit of the multilevel model was better than that of the traditional linear model. Conclusion The mental health of nursing staff is not only affected by individual level factors, but also affected by regional level factors. It suggests that combining different levels of intervention measures can upscale the effect of improving mental health in nursing staff.

Background In recent years, road traffic injury (RTI) has become a serious public health problem in China, and the factors affecting deaths caused by RTI are also complicated. Objective This study is designed to identify factors of RTI fatality risk and establish a road user fatality risk prediction model. Methods The data of traffic accident casualties in Pudong New Area of Shanghai from 2010 to 2016 were collected retrospectively, and the related impact factors of RTI were collected. Logistic regression was used to screen the selected factors of RTI fatality risk. A nomogram of RTI fatality risk was established, the consistency and accuracy of the model was evaluated by C-index and bootstrap internal verification, and a sensitivity analysis was also conducted. Results A total of 3521 casualties in traffic accidents were included in the study. The logistic regression results showed that age of victims, medical rescue distance, road type, transport means, injured body part, time of accident, and weekday/weekend affected RTI death risk (P<0.05). The nomogram model for RTI death risk showed that the most affecting factors were injured body part (especially head and neck injury), followed by age, transportation means, medical rescue distance, road type, time of accident, and weekday/weekend. The C-index of the model was 0.790, indicating high prediction accuracy and good fitness. The nomogram model for RTI death risk of head and neck injury showed that the score scales of all included factors expanded, the most prominent (most affecting) one was age; the RTI fatality risk of different road types changed, where urban road became the most dangerous road type; in addition, walking was the transportation means with the greatest risk of RTI fatality from head and neck injury. The results of the sensitivity analysis on accidents with varied casualties confirmed the robustness of the model. Conclusion The road user fatality risk of RTI is affected by many factors. As a simple tool to predict fatality risk of RTI, the nomogram based on logistic regression has certain reference significance for road traffic safety.

Major public health emergencies have challenged the public health emergency response capacity of rural areas. Through the investigation and research on the epidemic prevention and control situation in a rural area in Hubei, 93 households were randomly sampled by questionnaire survey method, and 10 households were interviewed by the semi-structured interview method. The data were analyzed by cross-analysis and descriptive analysis. The results showed that the main body involved in epidemic prevention and control in rural areas was single, and the epidemic prevention and control measures mostly adopted simple measures such as closed management. It was difficult to implement epidemic prevention and control measures in the village. The village doctors assumed basic medical and health service functions but were not highly motivated. Sanitation facilities were lacking, but the social comprehensive publicity of epidemic prevention and control safety education and infectious disease prevention knowledge was effective. In the post-epidemic era, it is necessary to increase the public health and health awareness of rural residents through strengthen publicity, improve and optimize the rural public health system, build a rural public health community, and enhance village doctors’ professional quality, so as to promote the emergency response capacity of rural public health.